{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides a tutorial for:\n",
    "1. training a ScribblePrompt-UNet model from scratch on a (example) dataset \n",
    "2. finetuning ScribblePrompt-UNet on a new (example) dataset\n",
    "3. monitoring metrics recorded during training \n",
    "\n",
    "We use the [`pylot`](https://github.com/JJGO/pylot) library for experiment management."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "import scribbleprompt\n",
    "\n",
    "import yaml\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "# The training code makes extensive use of the pylot library  \n",
    "# https://github.com/JJGO/pylot\n",
    "import pylot\n",
    "import pylot.pandas\n",
    "import pylot.analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "The details of a training run are specified by a yaml config file. See `../configs` for examples. \n",
    "\n",
    "We use tasks from the [WBC Dataset](https://github.com/zxaoyou/segmentation_WBC) for training and evaluation as an example in this tutorial. To use your own data, you should modify the list of `train_tasks` and `val_tasks` in the `data` section of your config to list your own dataloader classes.\n",
    "\n",
    "## Command Line Training\n",
    "To train a model scratch (on WBC) from the command line, run:\n",
    "```\n",
    "python scribbleprompt/experiment/unet.py -config train_unet.yaml \n",
    "```\n",
    "\n",
    "To finetune ScribblePrompt-UNet (on WBC) from the command line, run:\n",
    "```\n",
    "python scribbleprompt/experiment/unet.py -config finetune_unet.yaml \n",
    "```\n",
    "\n",
    "Notes:\n",
    "* You will need to modify the paths in the configs before running (`CTRL-F` \"#update this!\")\n",
    "* The experiment runner will automatically create a folder for the training run based on the start time and hash of the config file\n",
    "* If the training run is interupted you can resume using `python scribbleprompt/experiment/unet.py -resume -config [PATH TO EXPERIMENT FOLDER]`\n",
    "* The only difference between `train_unet.yaml` and `finetune_unet.yaml` is the `pretrained_weights` field\n",
    "\n",
    "## Jupyter Training\n",
    "\n",
    "Alternatively, you can train within a jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the config for the training run\n",
    "config = yaml.safe_load(open(\"../configs/train_unet.yaml\", \"r\"))\n",
    "\n",
    "# update the output folder path\n",
    "config['log']['root'] = \"./scratch\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Intel MKL extensions not available for NumPy</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;31mIntel MKL extensions not available for NumPy\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Intel MKL extensions not available for SciPy</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;31mIntel MKL extensions not available for SciPy\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Using slow Pillow instead of Pillow-SIMD</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;31mUsing slow Pillow instead of Pillow-SIMD\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/autofs/cluster/dalcalab2/users/hew19/interseg/work/public_code/ScribblePrompt/notebooks/scratch/20241231_132723-9ID2-d5b928a8435737196a1fd5c5b4c5c4f6\n",
      "Finished init....\n"
     ]
    }
   ],
   "source": [
    "from scribbleprompt.experiment.unet import ScribblePromptExperiment\n",
    "\n",
    "# Set the GPU to use\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = str(0)\n",
    "\n",
    "# Create the experiment runner object\n",
    "exp = ScribblePromptExperiment.from_config(config)\n",
    "\n",
    "# A unique experiment folder will be created using the current time and hash of the config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To train the model\n",
    "# exp.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can reload the experiment object later using \n",
    "```\n",
    "exp = ScribblePromptExperiment(FULL_EXPERIMENT_FOLDER_PATH)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monitoring Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section shows how to load the metrics recorded during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d1230919dd44380857c9db83d0eac6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>augmentations</th>\n",
       "      <th>data</th>\n",
       "      <th>samples_per_epoch</th>\n",
       "      <th>sampling</th>\n",
       "      <th>superpixel_prob</th>\n",
       "      <th>train_tasks</th>\n",
       "      <th>val_tasks</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>num_workers</th>\n",
       "      <th>pin_memory</th>\n",
       "      <th>...</th>\n",
       "      <th>prob_scribble</th>\n",
       "      <th>scribble_generators</th>\n",
       "      <th>bf16</th>\n",
       "      <th>epochs</th>\n",
       "      <th>eval_freq</th>\n",
       "      <th>grad_accumulation</th>\n",
       "      <th>prompt_iter</th>\n",
       "      <th>path</th>\n",
       "      <th>root</th>\n",
       "      <th>from_pretrained_weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[{\"RandomAffine\": {\"degrees\": [0, 360], \"p\": 0...</td>\n",
       "      <td>scribbleprompt.datasets.superpixel.SuperpixelM...</td>\n",
       "      <td>1000</td>\n",
       "      <td>hierarchical</td>\n",
       "      <td>0.5</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.interactions.scrib...</td>\n",
       "      <td>True</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>/autofs/cluster/dalcalab2/users/hew19/work/int...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[{\"RandomAffine\": {\"degrees\": [0, 360], \"p\": 0...</td>\n",
       "      <td>scribbleprompt.datasets.superpixel.SuperpixelM...</td>\n",
       "      <td>1000</td>\n",
       "      <td>hierarchical</td>\n",
       "      <td>0.5</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>[{\"_class\": \"scribbleprompt.interactions.scrib...</td>\n",
       "      <td>False</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>/autofs/cluster/dalcalab2/users/hew19/work/int...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        augmentations  \\\n",
       "7   [{\"RandomAffine\": {\"degrees\": [0, 360], \"p\": 0...   \n",
       "13  [{\"RandomAffine\": {\"degrees\": [0, 360], \"p\": 0...   \n",
       "\n",
       "                                                 data  samples_per_epoch  \\\n",
       "7   scribbleprompt.datasets.superpixel.SuperpixelM...               1000   \n",
       "13  scribbleprompt.datasets.superpixel.SuperpixelM...               1000   \n",
       "\n",
       "        sampling  superpixel_prob  \\\n",
       "7   hierarchical              0.5   \n",
       "13  hierarchical              0.5   \n",
       "\n",
       "                                          train_tasks  \\\n",
       "7   [{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...   \n",
       "13  [{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...   \n",
       "\n",
       "                                            val_tasks batch_size  num_workers  \\\n",
       "7   [{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...          8           16   \n",
       "13  [{\"_class\": \"scribbleprompt.datasets.wbc.WBC\",...          8           16   \n",
       "\n",
       "    pin_memory  ...  prob_scribble  \\\n",
       "7         True  ...            0.5   \n",
       "13        True  ...            0.5   \n",
       "\n",
       "                                  scribble_generators   bf16 epochs eval_freq  \\\n",
       "7   [{\"_class\": \"scribbleprompt.interactions.scrib...   True   1000        10   \n",
       "13  [{\"_class\": \"scribbleprompt.interactions.scrib...  False   1000        10   \n",
       "\n",
       "    grad_accumulation prompt_iter  \\\n",
       "7               False           5   \n",
       "13              False           5   \n",
       "\n",
       "                                                 path root  \\\n",
       "7   /autofs/cluster/dalcalab2/users/hew19/work/int...  NaN   \n",
       "13  /autofs/cluster/dalcalab2/users/hew19/work/int...  NaN   \n",
       "\n",
       "    from_pretrained_weights  \n",
       "7                      True  \n",
       "13                    False  \n",
       "\n",
       "[2 rows x 50 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = pylot.analysis.ResultsLoader()\n",
    "\n",
    "# Load configs for all the exeriments in the output folder\n",
    "dfc = rs.load_configs('/autofs/cluster/dalcalab2/users/hew19/work/interseg-public/output/')\n",
    "\n",
    "# Select some experiments\n",
    "dfc = dfc.select(\n",
    "    lr=1e-4,\n",
    "    batch_size=8,\n",
    "    num_workers=16,\n",
    "    epochs=1000,\n",
    "    superpixel_prob=0.5,\n",
    "    loss_func='scribbleprompt.loss.FocalDiceLoss'\n",
    ")\n",
    "\n",
    "# Create flag for plotting\n",
    "dfc['from_pretrained_weights'] = ~dfc['pretrained_weights'].isna()\n",
    "\n",
    "dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pretrained_weights</th>\n",
       "      <th>bf16</th>\n",
       "      <th>path</th>\n",
       "      <th>from_pretrained_weights</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>/autofs/cluster/dalcalab2/users/hew19/work/int...</td>\n",
       "      <td>True</td>\n",
       "      <td>/autofs/cluster/dalcalab2/users/hew19/work/int...</td>\n",
       "      <td>True</td>\n",
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       "      <th>13</th>\n",
       "      <td>None</td>\n",
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       "      <td>/autofs/cluster/dalcalab2/users/hew19/work/int...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   pretrained_weights   bf16  \\\n",
       "7   /autofs/cluster/dalcalab2/users/hew19/work/int...   True   \n",
       "13                                               None  False   \n",
       "\n",
       "                                                 path  from_pretrained_weights  \n",
       "7   /autofs/cluster/dalcalab2/users/hew19/work/int...                     True  \n",
       "13  /autofs/cluster/dalcalab2/users/hew19/work/int...                    False  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the difference between the experiment configs \n",
    "# .select and .drop_constant are useful functions from pylot.pandas\n",
    "dfc.drop_constant()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_minor": 0
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     "metadata": {},
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    }
   ],
   "source": [
    "# Load the metrics for the selected experiments\n",
    "df = rs.load_metrics(dfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2e0585e6e0>"
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     "execution_count": 10,
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      "text/plain": [
       "<Figure size 1829.83x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(\n",
    "    data = df,\n",
    "    x = 'epoch',\n",
    "    y = 'loss',\n",
    "    col = 'phase',\n",
    "    kind='line',\n",
    "    hue = 'from_pretrained_weights',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note about **phases**:\n",
    "* `train` = the training split of the training tasks with (data and superpixel label) augmentations\n",
    "* `val_id` = the validation split of the training tasks without any augmentations\n",
    "* `val_od` = the validation split of the validation tasks without any augmentations \n",
    "\n",
    "The training tasks and validation tasks are specified in the experiment config. **In this example**, we used the cytoplasm label for training and nucleus label for validation from the [WBC dataset](https://github.com/zxaoyou/segmentation_WBC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2e05718130>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1829.83x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.relplot(\n",
    "    data = df,\n",
    "    x = 'epoch',\n",
    "    y = 'dice_score',\n",
    "    col = 'phase',\n",
    "    kind='line',\n",
    "    hue = 'from_pretrained_weights',\n",
    ")\n",
    "g.set(ylim=(0.2, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sp_test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
